WO2024067034A1

WO2024067034A1 - Hearing detection method, apparatus and system

Info

Publication number: WO2024067034A1
Application number: PCT/CN2023/117902
Authority: WO
Inventors: 刘俊材; 赵安; 林龙
Original assignee: 华为技术有限公司
Priority date: 2022-09-27
Filing date: 2023-09-11
Publication date: 2024-04-04
Also published as: CN117814788A

Abstract

The present application provides a hearing detection method, apparatus and system, and mainly relates to the interaction between an audio output device, a signal acquisition device, and a computing device. Specifically, the audio output device outputs a sound signal to a user; the signal acquisition device acquires an electroencephalogram signal generated by the user in response to the sound signal and transmits the acquired electroencephalogram signal to the computing device; and the computing device determines the hearing condition of the user according to the received electroencephalogram signal. The audio output device may be, for example, an earphone, the signal acquisition device may be, for example, smart glasses, and the computing device may be, for example, a mobile phone. According to the hearing detection method, apparatus and system provided by the present application, automatic evaluation of the hearing condition of the user can be realized by utilizing cooperation of the multiple devices, that is to say, the user can complete hearing detection conveniently and simply by using a common electronic device in daily life.

Description

A hearing detection method, device and system

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 27, 2022, with application number 202211185227.9 and application name “A hearing detection method, device and system”, the entire contents of which are incorporated by reference into this application.

Technical Field

The embodiments of the present application relate to the field of hearing detection, and more specifically, to a hearing detection method, device and system.

Background technique

Hearing loss has become an unprecedented risk in today's world. Recently, the World Health Organization released the first "World Hearing Report", which shows that one in five people in the world currently have hearing loss, and hearing loss affects more than 1.5 billion people worldwide. Therefore, hearing loss detection is becoming more and more important. Traditional hearing test methods can be divided into two categories: one is subjective testing, such as pure tone audiometry and speech testing. This type of hearing test method relies on the subject's understanding of the test method and cooperation with the test, and the accuracy depends on the experience of the audiologist; the other type does not require the subject to understand and the audiologist to conduct objective tests, such as otoacoustic emissions, auditory brainstem evoked potentials and other tests, but requires special equipment such as otoacoustic emission monitors and auditory brainstem response testers for clinical testing, and the subjects cannot perform hearing tests in their daily lives. Therefore, how to conveniently conduct hearing assessments for subjects has become an urgent problem to be solved.

Summary of the invention

The embodiments of the present application provide a hearing detection method, apparatus and system, which can utilize multiple devices to collaboratively implement an assessment of a user's hearing condition. That is, the user can complete the hearing detection by using common electronic devices in daily life, which is convenient and simple.

In a first aspect, a hearing detection system is provided, which includes an audio output device, a signal acquisition device and a computing device, wherein the audio output device is used to: receive first information sent by the computing device, the first information is used to instruct the audio output device to output a sound signal to the user; the signal acquisition device is used to: receive second information sent by the computing device, the second information is used to instruct the signal acquisition device to collect an electroencephalogram signal, which is an electroencephalogram signal generated by the user in response to the sound signal; the signal acquisition device is also used to: transmit the electroencephalogram signal to the computing device; the computing device is used to: determine the hearing condition of the user based on the electroencephalogram signal.

Among them, the audio output device is a device that can emit audio signals, such as headphones; the signal acquisition device is a device that can collect EEG signals, and the device is provided with electrodes that can collect EEG signals generated by the user, such as smart glasses; the computing device is a device with a CPU, such as a mobile phone, computer, etc.

It should be understood that the sound signal is a standard test audio. The standard test audio can be, for example, a sound of a fixed frequency and a fixed duration. The frequency of the sound signal can be, for example, 1kHz, 4kHz, 8kHz or 12kHz, and the duration of the sound signal can be, for example, 1s or 2s, that is, the sound signal can be a short pure tone of a fixed frequency. By setting the sound signal from low frequency to high frequency, it is possible to detect whether the user can hear sounds of each frequency, thereby more accurately measuring whether the user's hearing is normal. In addition, the audio output device can repeatedly output the sound signal at intervals of a preset period (for example, 5s).

It should be noted that before conducting the hearing test, the computing device, the audio output device and the signal acquisition device can be networked and time-synchronized via a distributed soft bus.

Based on the above technical solution, the audio output device can output sound signals to the user, the signal acquisition device can collect the EEG signals generated by the user in response to the sound signals, and send the collected EEG signals to the computing device, and the computing device determines the user's hearing condition based on the received EEG signals. The method provided in the present application can use multiple devices to collaborate to achieve automatic evaluation of the user's hearing condition, that is, the user can complete the hearing test by using common electronic devices in daily life, which is convenient and simple. In addition, since the audio output device and the signal acquisition device are independent of each other, the noise generated by the working current of the audio output device on the measurement circuit can be reduced, thereby improving the signal-to-noise ratio.

In combination with the first aspect, in some implementations of the first aspect, the signal acquisition device includes a first electrode, a second electrode, a third electrode, and a fourth electrode, the first electrode contacts one ear of the user, the second electrode contacts the other ear of the user, the other ear of the user is used to receive the sound signal, the third electrode and the fourth electrode contact the scalp of the user, and the fourth electrode and the third electrode are located on the head of the user. Different locations of the skin;

The EEG signal measured by the first electrode is a first signal, the EEG signal measured by the second electrode is a second signal, the EEG signal measured by the third electrode is a third signal, and the EEG signal measured by the fourth electrode is a fourth signal; the computing device can also be used to determine the user's hearing condition based on the first signal, the second signal, the third signal and the fourth signal.

It should be understood that the electrodes of the signal acquisition device can be divided into primary electrodes, secondary electrodes and reference electrodes. The first electrode mentioned above is the primary electrode, the second electrode mentioned above is the secondary electrode, and the third electrode and the fourth electrode mentioned above are the reference electrodes. Among them, the electrode on the audio sounding side is the secondary electrode, the electrode on the opposite side of the audio sounding is the primary electrode, and the electrodes at other positions are reference electrodes.

It should be noted that, since the EEG signal measured by the secondary electrode (i.e., the second electrode) has strong noise interference, the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, the computing device is also used to determine the user's hearing condition based on the first signal, the third signal, and the fourth signal.

Based on the above technical solution, the computing device can determine the user's hearing condition based on the EEG signals collected by the signal acquisition device at different locations, thereby being able to more accurately determine the user's hearing condition and complete automatic hearing assessment of the user.

In combination with the first aspect, in certain implementations of the first aspect, the computing device is further used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the second signal and the third signal to obtain a second differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; calculate the difference between the second signal and the fourth signal to obtain a fourth differential signal; determine the user's hearing condition based on the first differential signal, the second differential signal, the third differential signal and the fourth differential signal.

It should be noted that, since the EEG signal measured by the secondary electrode (i.e., the second electrode) has strong noise interference, the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, the computing device may also be used to determine the user's hearing condition based on the first differential signal and the third differential signal.

Based on the above technical solution, the computing device can further calculate the differential signal of the EEG signal collected by the signal acquisition device, and determine the user's hearing condition based on the differential signal, thereby being able to more accurately determine the user's hearing condition and complete the user's automatic hearing assessment.

In combination with the first aspect, in certain implementations of the first aspect, the computing device is further used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the second signal and the third signal to obtain a second differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; calculate the difference between the second signal and the fourth signal to obtain a fourth differential signal; determine the user's hearing condition based on the first differential signal, the second differential signal, the third differential signal, the fourth differential signal and the first reference signal; wherein the first reference signal is the difference between the first signal and the first basic signal, and the first basic signal is the average of the third signal and the fourth signal.

It should be noted that, due to the strong noise interference of the EEG signal measured by the secondary electrode (i.e., the second electrode), the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, the computing device is also used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; calculate the average value of the third signal and the fourth signal to obtain a first basic signal; calculate the difference between the first signal and the first basic signal to obtain a first reference signal; determine the user's hearing condition based on the first differential signal, the third differential signal, and the first reference signal.

It should be understood that the number of electrodes in the present application is merely exemplary, and the present application may also include more additional electrodes.

Based on the above technical solution, in the present application, by adding additional electrodes to participate in the user's hearing assessment, the test time can be reduced or the test accuracy can be improved under the same test time.

In combination with the first aspect, in certain implementations of the first aspect, the standard deviation of the first differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the first differential signal and the first reference signal is greater than the second threshold; the standard deviation of the second differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the second differential signal and the first reference signal is greater than the second threshold; the standard deviation of the third differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the third differential signal and the first reference signal is greater than the second threshold; the standard deviation of the fourth differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the fourth differential signal and the first reference signal is greater than the second threshold.

Exemplarily, the first threshold may be 2, and the second threshold may be 0.8.

It is understandable that if the user performs other activities during the hearing test, resulting in noise differences or voltage differences in the data collected by each electrode, the correlation between the reference signal and the differential signal will become smaller and the variance will become larger. Therefore, by limiting the standard deviation and correlation coefficient of the first differential signal, the second differential signal, the third differential signal, and the fourth differential signal to meet certain requirements, data with low signal quality can be discarded and data that meets certain requirements can be retained, thereby improving the accuracy of the user's hearing assessment.

In combination with the first aspect, in some implementations of the first aspect, the signal acquisition device is further used to: transmit the EEG signal to the computing device when the number of signal acquisition times of the signal acquisition device is greater than a third threshold.

Exemplarily, the third threshold may be 900.

It is understandable that when the signal acquisition device is collecting signals, the number of collection times should meet certain requirements. For example, the signal acquisition device should collect signals more than 900 times. The more EEG signals the signal acquisition device collects, the more references the computing device will have when determining the user's hearing condition, which will be more conducive to the computing device evaluating the user's hearing condition.

In combination with the first aspect, in some implementations of the first aspect, the audio output device is connected to the computing device in a wireless manner, and the signal acquisition device is connected to the computing device in a wireless manner.

In combination with the first aspect, in certain implementations of the first aspect, the audio output device is further used to: output a sound signal starting from a first moment; the signal acquisition device is further used to: acquire an electroencephalogram signal starting from a second moment; and the time difference between the first moment and the second moment is less than a fourth threshold.

Exemplarily, the fourth threshold may be 1.

Based on the above technical solution, in the present application, the electrodes do not need to be connected by wires, and wireless measurement can be achieved. When the audio output device is connected to the computing device wirelessly, and the signal acquisition device is connected to the computing device wirelessly, it is necessary to synchronize the time between the devices, so as to accurately determine the user's hearing condition and complete the user's automatic hearing assessment.

In combination with the first aspect, in certain implementations of the first aspect, the computing device is further used to: detect the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device; if it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, output a prompt message to remind the user; if it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device does not meet the wearing standard, output a prompt message to remind the user.

It is understandable that the audio output device and the signal acquisition device can use their own sensors, and the computing device can use the sensors to detect the user's connection status and wearing posture. If the connection status and wearing posture of the audio output device and the signal acquisition device do not meet the requirements, the user needs to be prompted to make adjustments so that the user can perform accurate hearing testing.

In combination with the first aspect, in certain implementations of the first aspect, the computing device is also used to: filter and superimpose the EEG signal to determine the waveform of the EEG signal; and classify the waveform of the EEG signal using a preset algorithm to determine the user's hearing condition.

It should be noted that the preset algorithm can be a commonly used trained machine learning algorithm, for example, a neural network classification algorithm, a Bayesian classification algorithm, a support vector machine SVM algorithm, etc.

It is understandable that when measuring the hearing of the user's right ear, the computing device can filter and superimpose the first differential signal obtained by multiple measurements to obtain the waveform of the EEG signal of the user's right ear. The waveform is classified by the classifier to determine whether the waveform is a normal waveform or an abnormal waveform, thereby determining whether the user's hearing is normal.

In a second aspect, a hearing detection method is provided, which is applied to a computing device, and the method includes: sending first information to an audio output device, the first information being used to instruct the audio output device to output a sound signal to a user; sending second information to a signal acquisition device, the second information being used to instruct the signal acquisition device to collect an electroencephalogram (EEG) signal; receiving the electroencephalogram (EEG) signal sent by the signal acquisition device, the electroencephalogram (EEG) signal being an EEG signal generated by the user in response to the sound signal; and determining the user's hearing condition based on the electroencephalogram (EEG) signal.

In combination with the second aspect, in certain implementations of the second aspect, determining the user's hearing condition based on the EEG signal includes: determining the user's hearing condition based on a first signal, a second signal, a third signal, and a fourth signal; wherein the first signal is an EEG signal obtained by the signal acquisition device measuring the opposite side of the sound signal, the second signal is an EEG signal obtained by the signal acquisition device measuring the side of the sound signal, the third signal and the fourth signal are EEG signals obtained by the signal acquisition device measuring the user's scalp, and the measurement position of the third signal is different from the measurement position of the fourth signal.

It should be noted that, since the EEG signal measured by the secondary electrode (i.e., the second electrode) has strong noise interference, the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, determining the user's hearing condition based on the EEG signal may also include: determining the user's hearing condition based on the first signal, the third signal, and the fourth signal.

In combination with the second aspect, in certain implementations of the second aspect, determining the user's hearing condition based on the first signal, the second signal, the third signal and the fourth signal includes: calculating the difference between the first signal and the third signal to obtain a first differential signal; calculating the difference between the second signal and the third signal to obtain a second differential signal; calculating the difference between the first signal and the fourth signal to obtain a third differential signal; calculating the difference between the second signal and the fourth signal to obtain a fourth differential signal; determining the user's hearing condition based on the first differential signal, the second differential signal, the third differential signal and the fourth differential signal.

It should be noted that, since the EEG signal measured by the secondary electrode (i.e., the second electrode) has strong noise interference, the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, determining the user's hearing condition based on the first signal, the third signal, and the fourth signal may also include: determining the user's hearing condition based on the first differential signal and the third differential signal. number to determine the user's hearing condition.

In combination with the second aspect, in certain implementations of the second aspect, determining the user's hearing condition based on the first signal, the second signal, the third signal and the fourth signal includes: calculating the difference between the first signal and the third signal to obtain a first differential signal; calculating the difference between the second signal and the third signal to obtain a second differential signal; calculating the difference between the first signal and the fourth signal to obtain a third differential signal; calculating the difference between the second signal and the fourth signal to obtain a fourth differential signal; determining the user's hearing condition based on the first differential signal, the second differential signal, the third differential signal, the fourth differential signal and the first reference signal; wherein the first reference signal is the difference between the first signal and the first basic signal, and the first basic signal is the average of the third signal and the fourth signal.

It should be noted that, due to the strong noise interference of the EEG signal measured by the secondary electrode (i.e., the second electrode), the computing device may not consider the EEG signal measured by the secondary electrode (i.e., the second electrode) when determining the user's hearing condition. In other words, the determination of the user's hearing condition based on the first signal, the third signal, and the fourth signal may also include: calculating the difference between the first signal and the third signal to obtain a first differential signal; calculating the difference between the first signal and the fourth signal to obtain a third differential signal; calculating the average value of the third signal and the fourth signal to obtain a first basic signal; calculating the difference between the first signal and the first basic signal to obtain a first reference signal; and determining the user's hearing condition based on the first differential signal, the third differential signal, and the first reference signal.

In combination with the second aspect, in certain implementations of the second aspect, the standard deviation of the first differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the first differential signal and the first reference signal is greater than the second threshold; the standard deviation of the second differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the second differential signal and the first reference signal is greater than the second threshold; the standard deviation of the third differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the third differential signal and the first reference signal is greater than the second threshold; the standard deviation of the fourth differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the fourth differential signal and the first reference signal is greater than the second threshold.

In combination with the second aspect, in some implementations of the second aspect, the audio output device is connected to the computing device wirelessly, and the signal acquisition device is connected to the computing device wirelessly.

In combination with the second aspect, in certain implementations of the second aspect, the method further includes: controlling the audio output device to output a sound signal starting from a first moment; controlling the signal acquisition device to collect an EEG signal starting from a second moment; and the time difference between the first moment and the second moment is less than a fourth threshold.

In combination with the second aspect, in certain implementations of the second aspect, before sending the first information to the audio output device, the method also includes: detecting the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device; if it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, outputting a prompt message to remind the user; if it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device does not meet the wearing standard, outputting a prompt message to remind the user.

In combination with the second aspect, in certain implementations of the second aspect, determining the user's hearing condition based on the EEG signal includes: filtering and superimposing the EEG signal to determine the waveform of the EEG signal; and classifying the waveform of the EEG signal using a preset algorithm to determine the user's hearing condition.

In a third aspect, a hearing detection device is provided, which includes a receiving unit, a sending unit and a processing unit. The sending unit is used to: send first information to an audio output device, wherein the first information is used to instruct the audio output device to output a sound signal to a user; send second information to a signal acquisition device, wherein the second information is used to instruct the signal acquisition device to acquire an electroencephalogram signal; the receiving unit is used to: receive the electroencephalogram signal sent by the signal acquisition device, wherein the electroencephalogram signal is an electroencephalogram signal generated by the user in response to the sound signal; and the processing unit is used to: determine the user's hearing condition based on the electroencephalogram signal.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is further used to: determine the user's hearing condition based on the first signal, the third signal and the fourth signal; wherein the first signal is an electroencephalogram signal obtained by the signal acquisition device measuring the opposite side of the sound signal, the third signal and the fourth signal are electroencephalogram signals obtained by the signal acquisition device measuring the user's scalp, and the measurement position of the third signal is different from the measurement position of the fourth signal.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; calculate the average value of the third signal and the fourth signal to obtain a first basic signal; calculate the difference between the first signal and the first basic signal to obtain a first reference signal; determine the user's hearing condition based on the first differential signal, the third differential signal and the first reference signal.

In combination with the third aspect, in certain implementations of the third aspect, the standard deviation of the first differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the first differential signal and the first reference signal is greater than the second threshold; the standard deviation of the third differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the third differential signal and the first reference signal is greater than the second threshold.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; and determine the user's hearing condition based on the first differential signal and the third differential signal.

In combination with the third aspect, in some implementations of the third aspect, the audio output device is connected to the computing device wirelessly, and the signal acquisition device is connected to the computing device wirelessly.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to: control the audio output device to output a sound signal starting from a first moment; control the signal acquisition device to collect EEG signals starting from a second moment; and the time difference between the first moment and the second moment is less than a fourth threshold.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to: detect the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device; if it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, output a prompt message to remind the user; if it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device does not meet the wearing standard, output a prompt message to remind the user.

In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to: filter and superimpose the EEG signal to determine the waveform of the EEG signal; classify the waveform of the EEG signal using a preset algorithm to determine the user's hearing condition.

In a fourth aspect, a hearing detection device is provided, comprising: an audio output module, a signal acquisition module and a computing module. The audio output module is used to execute the steps executed by the audio output device in the first aspect or any possible implementation of the first aspect; the signal acquisition module is used to execute the steps executed by the signal acquisition device in the first aspect or any possible implementation of the first aspect; the computing module is used to execute the steps executed by the computing device in the first aspect or any possible implementation of the first aspect, or to execute the steps executed by the computing device in the second aspect or any possible implementation of the second aspect.

In a fifth aspect, a hearing detection device is provided, comprising: a processor, the processor is coupled to a memory, the memory is used to store programs or instructions, when the program or instructions are executed by the processor, the control device implements the method of the above-mentioned second aspect or any possible implementation method of the second aspect.

In a sixth aspect, a chip is provided, which includes a processor and a data interface, and the processor reads instructions stored in a memory through the data interface to execute the method of the above-mentioned second aspect or any possible implementation method of the second aspect.

Optionally, as an implementation method, the chip may also include a memory, in which instructions are stored, and the processor is used to execute the instructions stored in the memory. When the instructions are executed, the processor is used to execute the method in the second aspect or any possible implementation method of the second aspect.

In the seventh aspect, a computer-readable storage medium is provided, in which a computer program or instruction is stored. When the computer program or instruction is executed, the method in the above-mentioned second aspect or any possible implementation manner of the second aspect is implemented.

In an eighth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the second aspect or any possible implementation of the second aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a hearing detection system provided in an embodiment of the present application.

FIG2 is a schematic flow chart of a hearing detection method provided in an embodiment of the present application.

FIG3 is a schematic flow chart of another hearing detection method provided in an embodiment of the present application.

FIG. 4 is a schematic diagram of a standard for placement of EEG electrodes provided in an embodiment of the present application.

FIG5 is a schematic flow chart of another hearing detection method provided in an embodiment of the present application.

FIG. 6 is a BAEP classification diagram of an acoustic neuroma provided in an embodiment of the present application.

FIG. 7 is a schematic diagram of a hearing detection device provided in an embodiment of the present application.

FIG. 8 is a schematic diagram of another hearing detection device provided in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings.

As mentioned in the background technology section, more and more people are currently affected by hearing loss, so more and more people are paying attention to the field of hearing testing. Current hearing testing methods can be mainly divided into two categories: subjective testing and objective testing.

Subjective testing methods can include pure tone audiometry, speech testing, etc. Pure tone audiometry mainly involves Under these conditions, the subject is given multiple stimulation signals (the frequency of the stimulation signals is generally in the range of 125Hz to 8000Hz), and the minimum sound signal intensity that the subject can hear is compared with the normal level to determine whether the hearing is impaired. This method can quickly and accurately determine the subject's hearing level and ear lesions, and more intuitively and comprehensively reflect the subject's hearing condition, but the subject needs to understand the test requirements and cooperate with the test, and the accuracy depends on the experience of the audiologist. The speech test method mainly checks whether the subject can hear speech sounds and whether he can recognize different speech sounds, that is, whether the subject can understand the meaning of the speech sounds. This method can provide more information about the patient's hearing function and speech discrimination, but its accuracy also needs to rely on the experience of the audiologist.

Objective testing methods may include otoacoustic emissions, auditory brainstem evoked potentials and other methods. Otoacoustic emissions can determine whether the hair cells of the cochlea are damaged or lesions by detecting the amount of energy transmitted from the cochlea. Otoacoustic emissions are often used for newborn hearing screening, and can also determine whether the function of the auditory efferent nerve is normal, but otoacoustic emissions only reflect the acoustic emission function of the cochlea, not the hearing condition, and rely on otoacoustic emission detectors for clinical examinations, and cannot be screened by themselves. Auditory brainstem evoked potentials assess auditory function by detecting a series of neurogenic electrical activities caused by sound stimulation in the auditory system from the inner ear cochlea to the auditory center of the cerebral cortex. Auditory brainstem evoked potentials can reflect peripheral hearing sensitivity and the neural conduction function of the brainstem auditory pathway, but they also rely on auditory brainstem response testers for clinical examinations and cannot be screened by themselves.

It is understandable that the auditory brainstem evoked potential can reflect the peripheral hearing sensitivity and the neural conduction function of the brainstem auditory pathway, that is, it can reflect whether the auditory nerve and conduction pathway, and the auditory cortex center are normal. In other words, the hearing test method of the auditory brainstem evoked potential can objectively reflect the hearing condition of the subject, but the hearing test method of the auditory brainstem evoked potential relies on the auditory brainstem response tester, and the subject cannot perform hearing tests in daily life.

Therefore, the present application provides a hearing detection method, device and system that can use multiple devices to collaborate to achieve automatic assessment of the user's hearing condition. In other words, the user can complete the hearing test by using common electronic devices in daily life, which is convenient and simple.

As shown in FIG1 , the hearing detection system 100 may include an audio output module 120 , a signal acquisition module 130 , and a hearing assessment module 140 . In some embodiments, the hearing detection system 100 may further include a pre-processing module 110 .

It can be understood that, in some embodiments, the preprocessing module 110, the audio output module 120, the signal acquisition module 130 and the hearing assessment module 140 can be integrated into the same device. In other embodiments, the audio output module 120, the signal acquisition module 130 and the hearing assessment module 140 can be integrated into different devices, and the preprocessing module 110 can be integrated into the same device as the hearing assessment module 140. Exemplarily, the audio output module 120 can be integrated into a first device, which can be, for example, an audio output device described below; the signal acquisition module 130 can be integrated into a second device, which can be, for example, a signal acquisition device described below; the preprocessing module 110 and the hearing assessment module 140 can be integrated into a third device, which can be, for example, a computing device described below.

The preprocessing module 110 is used to detect whether the connection status and wearing posture of the audio output module and the signal acquisition module meet the requirements. When the user starts to perform a hearing test on the computing device, the preprocessing module 110 can detect the connection status of the audio output device and the connection status of the signal acquisition device. If the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, the computing device needs to output a prompt message to remind the user to connect the audio output device and/or remind the user to connect the signal acquisition device to meet the hearing test requirements. When the user starts to perform a hearing test on the computing device, the preprocessing module 110 can detect the wearing posture of the audio output device and the wearing posture of the signal acquisition device. If the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device do not meet the specified requirements, the computing device needs to output a prompt message to remind the user to adjust the audio output device and/or the signal acquisition device to meet the hearing test requirements.

The audio output module 120 is used to output sound signals. The audio output module can output standard test audio to the ear (for example, emit sound of specific frequency and loudness), and can be connected to the hearing assessment module 140 in a wired or wireless manner.

The signal acquisition module 130 is used to collect the electrical signal generated by the brain stem after the user's ear receives the specific audio. The signal acquisition module 130 can collect the EEG signal and can be connected to the hearing assessment module 140 in a wired or wireless manner.

The hearing assessment module 140 is used to process the brain wave signals collected by the signal acquisition module 130 , identify different brain wave signals, and assess the hearing of the user. The hearing assessment module 140 can be connected to the audio output module 120 and the signal acquisition module 130 .

Fig. 2 is a schematic flow chart of a hearing detection method provided in an embodiment of the present application. The hearing detection method 300 mainly involves the interaction between an audio output device, a signal acquisition device and a computing device, and the method 200 may include S201 to S204.

S201: An audio output device receives first information sent by a computing device.

Specifically, the computing device sends first information, and the audio output device receives the first information, and the first information is used to instruct the audio output device to output a sound signal to the user. That is, after the computing device receives the first information, it can output a sound signal to the user. The audio output device can be connected to the computing device by wire or wirelessly, and the audio output device can be, for example, a headset, an in-ear headset, a Bluetooth headset, etc.

S202, the signal collection device receives second information sent by the computing device.

Specifically, the computing device sends the second information, and the signal acquisition device receives the second information, and the second information is used to instruct the signal acquisition device to collect EEG signals, which are EEG signals generated by the user in response to the sound signal. In other words, when the user receives the second information, the EEG signal can be collected. Among them, the signal acquisition device can be connected to the computing device by wire or wirelessly, and the signal acquisition device has electrodes at the forehead and the mastoid behind the ear (such as smart glasses) or a single electrode device that can be flexibly placed (such as a smart sensor).

Exemplarily, the signal acquisition device includes a first electrode, a second electrode, a third electrode and a fourth electrode, the first electrode contacts one ear of the user, the second electrode contacts the other ear of the user, and the other ear of the user is used to receive the sound signal, the third electrode and the fourth electrode contact the user's scalp, and the fourth electrode and the third electrode are located at different positions on the user's scalp, wherein the user's right ear is the side that receives the sound signal, and the user's left ear is the opposite side that receives the sound signal; the EEG signal measured by the first electrode is a first signal, the EEG signal measured by the second electrode is a second signal, the EEG signal measured by the third electrode is a third signal, and the EEG signal measured by the fourth electrode is a fourth signal.

It is understandable that, before S201 and S202, the method may further include: networking the computing device, the audio output device, and the signal acquisition device, and performing time synchronization.

After the computing device, audio output device and signal acquisition device are networked, the computing device is the main device, the audio output device and signal acquisition device are secondary devices, and the main device can send information to the secondary devices respectively, that is, the computing device can send information to the audio output device and signal acquisition device respectively.

It should be noted that the computing device, audio output device and signal acquisition device need to be time synchronized. When the computing device and the audio output device are connected via a wired connection, the time of the computing device, the audio output device and the signal acquisition device can be kept synchronized; when the computing device and the audio output device are connected wirelessly, the computing device, the audio output device and the signal acquisition device need to be time synchronized. The specific content of this part will be explained in Figure 6.

S203, the signal acquisition device transmits the EEG signal to the computing device.

This part of the content will be explained in detail in S305 and will not be repeated here.

Exemplarily, the signal acquisition device may include a first electrode, a second electrode, a third electrode, and a fourth electrode, wherein the first electrode contacts one ear of the user, the second electrode contacts the other ear of the user, and the other ear of the user is used to receive sound signals, the third electrode and the fourth electrode contact the scalp of the user, and the fourth electrode and the third electrode are located at different positions on the scalp of the user; the EEG signal measured by the first electrode is the first signal, the EEG signal measured by the second electrode is the second signal, the EEG signal measured by the third electrode is the third signal, and the EEG signal measured by the fourth electrode is the fourth signal. The signal acquisition device may send the collected first signal, second signal, third signal, and fourth signal to the computing device.

S204: The computing device determines the user's hearing condition based on the EEG signal.

In a possible implementation, the computing device may determine the hearing condition of the user according to the first signal, the third signal, and the fourth signal.

Optionally, the computing device calculates the difference between the first signal and the third signal to obtain a first differential signal; the computing device calculates the difference between the first signal and the fourth signal to obtain a third differential signal; and determines the user's hearing condition based on the first differential signal and the third differential signal.

Exemplarily, the computing device may filter and superimpose the first differential signal and the third differential signal acquired multiple times to obtain the user's EEG signal waveform, and classify the obtained waveform through a trained classifier to determine whether the waveform is a normal waveform or an abnormal waveform.

Optionally, the computing device calculates the difference between the first signal and the third signal to obtain a first differential signal; the computing device calculates the difference between the first signal and the fourth signal to obtain a third differential signal; the computing device calculates the average of the third signal and the fourth signal to obtain a first basic signal; the computing device calculates the difference between the first signal and the first basic signal to obtain a first reference signal; and determines the user's hearing condition based on the first differential signal, the third differential signal and the first reference signal. The specific process of the computing device determining the user's hearing condition based on the first differential signal, the third differential signal and the first reference signal can be referred to S306, which will not be repeated here.

In another possible implementation, the computing device may determine the hearing condition of the user according to the first signal, the second signal, the third signal, and the fourth signal.

Optionally, the computing device calculates the difference between the first signal and the third signal to obtain a first differential signal; the computing device calculates the difference between the second signal and the third signal to obtain a second differential signal; the computing device calculates the difference between the first signal and the fourth signal to obtain a third differential signal. The computing device calculates the difference between the second signal and the fourth signal to obtain a fourth differential signal; and determines the hearing condition of the user according to the first differential signal, the second differential signal, the third differential signal and the fourth differential signal.

Exemplarily, the computing device may filter and superimpose the first differential signal acquired multiple times, the second differential signal acquired multiple times, the third differential signal acquired multiple times, and the fourth differential signal acquired multiple times to obtain the user's EEG signal waveform, and classify the obtained waveform through a trained classifier to determine whether the waveform is a normal waveform or an abnormal waveform.

Optionally, the computing device calculates the difference between the first signal and the third signal to obtain a first differential signal; the computing device calculates the difference between the second signal and the third signal to obtain a second differential signal; the computing device calculates the difference between the first signal and the fourth signal to obtain a third differential signal; the computing device calculates the difference between the second signal and the fourth signal to obtain a fourth differential signal; the computing device calculates the average value of the third signal and the fourth signal to obtain a first basic signal; the computing device calculates the difference between the first signal and the first basic signal to obtain a first reference signal; and determines the user's hearing condition based on the first differential signal, the second differential signal, the third differential signal, the fourth differential signal and the first reference signal. The specific process of the computing device determining the user's hearing condition based on the first differential signal, the third differential signal and the first reference signal can be referred to S306, which will not be repeated here.

In an embodiment of the present application, the audio output device can output a sound signal to the user, the signal acquisition device can collect the EEG signal generated by the user in response to the sound signal, and send the collected EEG signal to the computing device, and the computing device determines the user's hearing condition based on the received EEG signal. The hearing detection method provided in the present application can use multiple devices to collaborate to achieve automatic evaluation of the user's hearing condition, that is, the user can complete the hearing test by using common electronic devices in daily life, which is convenient and simple. In addition, since the audio output device and the signal acquisition device are independent of each other, the noise generated by the working current of the audio output device to the measurement circuit can be reduced, thereby improving the signal-to-noise ratio.

Fig. 3 is a schematic flow chart of another hearing detection method provided in an embodiment of the present application. The hearing detection method 300 mainly involves the interaction between an audio output device, a signal acquisition device and a computing device, and the method 300 may include S301 to S307.

S301, a computing device detects a connection status and a wearing posture of an audio output device and a connection status and a wearing posture of a signal collection device.

When a user turns on hearing detection on a computing device (e.g., a mobile phone), it is necessary to detect the connection status of the audio output device and the connection status of the signal acquisition module, as well as the wearing posture of the user wearing the audio output device and the wearing posture of the signal acquisition device.

When a user turns on hearing detection on a computing device (e.g., a mobile phone), the computing device can detect the connection status of the audio output device (e.g., headphones) and the connection status of the signal acquisition device (e.g., smart glasses). If the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, the user needs to be reminded to connect the audio output device and/or the signal acquisition device. For example, a prompt box or pop-up window appears on the mobile phone indicating that the headphones are not connected and/or the smart glasses are not connected.

When the user turns on hearing detection on a computing device (e.g., a mobile phone), the computing device can also detect the posture of the user wearing an audio output device (e.g., headphones) and the posture of the user wearing a signal collection device (e.g., smart glasses). If the audio output device does not meet the wearing standards and/or the signal collection device does not meet the wearing standards, the user needs to be reminded to adjust the wearing status of the audio output device and/or the signal collection device until it meets the standards. For example, a prompt box or pop-up window appears on the mobile phone indicating that the headphones are not worn correctly and/or the smart glasses are not worn correctly.

After detecting that the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device meet the requirements, the positions of the audio output device and the signal acquisition device can be marked on the computing device.

When the user turns on the hearing test on a computing device (for example, a mobile phone), it is necessary to mark the location of the audio output device and the signal acquisition device on the computing device. In some embodiments, the computing device may display a prompt box, pop-up window, etc. on the computing device during the initialization phase of the hearing test to remind the user to mark the location of the audio output device and the signal acquisition device. In some embodiments, when the user performs a hearing test on the computing device, the user may also actively mark the location of the audio output device and the signal acquisition device. In some embodiments, the computing device may provide the locations of some audio output devices and signal acquisition devices, and the user may independently select the locations of the audio output device and the signal acquisition device.

Among them, the audio output device can be headphones, etc., and the sound position of the audio output device can be, for example, the left ear or the right ear; the signal acquisition device can include smart glasses, patches, helmets, etc., and the position of the signal acquisition device can be, for example, the left ear, the right ear, the top of the head, the forehead or other positions of the scalp.

As shown in FIG4, FIG4 shows a schematic diagram of a standard placement of EEG electrode positions. The positions of the signal acquisition device may include a primary acquisition position, a secondary acquisition position, and an additional acquisition position. The primary acquisition position of the signal acquisition device may include point A1 or A2 in FIG4, the secondary acquisition position of the signal acquisition device may include point A1 or A2 in FIG4, and the additional acquisition position of the signal acquisition device may include point A1 or A2 in FIG4. The set position is preferably placed in Cz and Fpz, but can also be placed in Nz, Fz, CPz, Pz, POz, Oz, Iz and other positions.

It can be understood that when the audio output device emits a sound signal at the user's left ear, the main collection position of the signal acquisition device is the user's right ear; when the audio output device emits a sound signal at the user's right ear, the main collection position of the signal acquisition device is the user's left ear.

Exemplarily, if the sound output position of the audio output device is the left ear, the main collection position of the signal acquisition device can be point A2 in Figure 4, and the secondary collection position of the signal acquisition device can be point A1 in Figure 4; if the sound output position of the audio output device is the right ear, the main collection position of the signal acquisition device can be point A1 in Figure 4, and the secondary collection position of the signal acquisition device can be point A2 in Figure 4.

In some embodiments, the signal acquisition device may include at least three sensors (e.g., a first sensor, a second sensor, and a third sensor), the first sensor may include a primary electrode (or secondary electrode) at position A1, the second sensor may include a secondary electrode (or primary electrode) at position A2, and the third sensor may include a reference electrode at position Cz or Fpz.

Optionally, the signal acquisition device may further include a fourth sensor, and the fourth sensor may include additional electrodes at positions such as Nz, Fz, CPz, Pz, POz, Oz, and Iz.

After detecting that the connection status and wearing posture of the audio output device and the signal acquisition device meet the requirements, the computing device, the audio output device and the signal acquisition device can be networked and time synchronized.

For example, computing devices, audio output devices, and signal acquisition devices can be networked through a distributed soft bus. The distributed soft bus provides a unified distributed communication capability for the interconnection between devices, creating conditions for senseless discovery and zero-wait transmission between devices. Relying on soft bus technology, it is easy to achieve multiple devices working together to complete a task, and the task can also be passed from one device to another device for continued execution. For users, there is no need to pay attention to the networking of multiple devices, and the soft bus can achieve self-discovery and self-networking.

In addition, time synchronization is required between the computing device, the audio output device, and the signal acquisition device. In the embodiment of the present application, the audio output device and the computing device can be connected by wire, so that the audio output device and the computing device are time synchronized; the signal acquisition device and the computing device can also be connected by wire, so that the signal output device and the computing device are time synchronized; and thus the time synchronization between the computing device, the audio output device, and the signal acquisition device can be achieved. In other embodiments, the audio output device and the computing device can be connected by wireless means, and the signal acquisition device and the computing device can also be connected by wireless means. Time synchronization is also required between the computing device, the audio output device, and the signal acquisition device. This part of the content will be specifically described in Figure 6.

S302: The computing device sends first information to the audio output device.

Specifically, after detecting that the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device meet the requirements, the computing device can send first information to the audio output device. After receiving the first information, the audio output device starts to emit an audio signal to one ear.

The first information may include first policy information, and the first policy information is used to indicate the timing of transmitting the first data set. In other words, the first policy information is used to indicate under what circumstances the audio output device sends the first data set to the computing device.

S303: The computing device sends second information to the signal collection device.

Specifically, the computing device may send the second information to the signal acquisition device, and after receiving the second information, the signal acquisition device starts to collect the EEG signal.

The second information may include second strategy information, and the second strategy information is used to indicate the timing of transmitting the second data set. In other words, the second strategy information is used to indicate under what circumstances the signal acquisition device sends the second data set to the computing device.

It should be noted that S302 and S303 can be executed simultaneously, that is, the computing device can simultaneously send the first information to the audio output device and send the second information to the signal acquisition device.

It can be understood that the computing device can control the audio output device to output standard test audio, and the signal acquisition device (for example, smart glasses) sets signal acquisition electrodes at corresponding positions such as the forehead and ears to obtain the electrical signals (i.e., EEG signals) generated by the brainstem after specific audio stimulation.

The audio signal is a standard test audio. The standard test audio may be, for example, a sound of a fixed frequency and a fixed duration. The frequency of the audio signal may be, for example, 1kHz, 4kHz, 8kHz, or 12kHz, and the duration of the audio signal may be, for example, 1s or 2s, that is, the audio signal may be a short sound/short pure tone of 1kHz, 4kHz, 8kHz, or 12kHz. By setting the audio signal from low frequency to high frequency, it is possible to detect whether the user can hear sounds of each frequency, thereby more accurately measuring whether the user's hearing is normal.

It should be understood that the audio output device can repeat the sound at a preset interval (for example, 5 seconds), and the signal acquisition device can repeatedly collect EEG signals. For example, if the sound duration of the audio signal is 1 second and the sound is repeated at an interval of 5 seconds, the signal acquisition device can collect EEG signals within one minute. The EEG signal was measured 10 times.

It should be noted that the time of the audio output device, the signal acquisition device and the computing device are synchronized, that is, the time error of the audio output device, the signal acquisition device and the computing device should be less than a preset threshold (for example, 1ms).

S304: The audio output device sends the collected first data set to the computing device.

It is understandable that when the computing device sends the first information to the audio output device, the first policy information may be carried in the first information. The first policy information may include the timing of transmitting the first data set. For example, the audio output device reports once a minute.

In some embodiments, if the user marks the sound signal emitting side on the computing device, the data collected by the audio output device (i.e., the first data set) may include the sound frequency and the sound emission timestamp. In other words, the computing device can know what kind of sound the audio output device emitted at what time and location. The sound emission timestamp may include the start time and the end time of the sound emission, and the sound frequency may be, for example, 1kHz, 2kHz, 4kHz, or 8kHz.

In some embodiments, if the audio output device autonomously determines the sound signal's sound emitting side position, the data collected by the audio output device (i.e., the first data set) may include the sound frequency, the sound emitting timestamp, and the sound emitting side. In other words, the computing device may know what kind of sound the audio output device emitted at what time and location.

For example, the data sent by the audio output device to the computing device may be a 1kHz sound emitted at the left ear from 06:00 to 06:01 (duration is 1 second). After receiving the data sent by the audio output device, the computing device may determine the relationship between the audio signal and the timestamp.

S305: The signal collection device sends the collected second data set to the computing device.

It is understandable that when the computing device sends the second information to the signal acquisition device, the second information may carry the second strategy information, which may include the timing of transmitting the second data set. Exemplarily, when the signal acquisition device acquires data a set number of times (e.g., 900 times), the second data set may be sent to the computing device.

It should be noted that the first policy information and the second policy information should be the same policy information. Exemplarily, the first policy information and the second policy information both indicate that data is transmitted back once a minute.

It is understandable that the data collected by the signal acquisition device (ie, the second data set) may include EEG signals and timestamps corresponding to the EEG signals. In other words, the computing device may know the EEG signals measured by the signal acquisition device at what time and location.

For example, if the sampling frequency of the EEG signal is 100, that is, 100 EEG signals can be collected in one second, then the data sent by the signal acquisition device to the computing device may be the 100 EEG signals collected at position A1 and the timestamps corresponding to the 100 EEG signals. After receiving the data sent by the signal acquisition device, the computing device can determine the relationship between the EEG signal and the timestamp.

S306: The computing device determines a target signal according to the first data set and the second data set.

Specifically, after receiving the data sent by the audio output device (ie, the first data set) and the data sent by the signal acquisition device (ie, the second data set), the computing device can process the received data to determine the retained signal (ie, the target signal).

It should be understood that the data sent by the audio output device (i.e., the first data set) includes the sound frequency, the timestamp of the utterance and the utterance side, and the data sent by the signal acquisition device (i.e., the second data set) includes the EEG signal, the timestamp corresponding to the EEG signal and the EEG signal acquisition location. When the computing device receives the first data set and the second data set, it can determine the user's EEG signal at a certain frequency based on the correspondence between the timestamp of the first data set and the timestamp of the second data set, thereby determining the user's hearing condition at that frequency.

In some embodiments, if there is an additional reference electrode, for example, the signal acquisition device includes a primary electrode at position A1, a secondary electrode at position A2, and a reference electrode at position Cz. The computing device can determine a first differential signal by subtracting a reference signal generated by the reference electrode from a primary signal generated by the primary electrode; and determine a second differential signal by subtracting a reference signal generated by the reference electrode from a secondary signal generated by the secondary electrode, thereby determining a target signal according to the first differential signal and the second differential signal.

Exemplarily, electrodes other than the two ears are selected as reference electrodes V _ref , the electrode on the audio sounding side is the secondary electrode V _i , and the electrode on the opposite side of the audio sounding is the primary electrode V _m . The first differential signal S ₁ and the second differential signal S ₂ are calculated, wherein S ₁ =V _m -V _ref , S ₂ =V _i -V _ref , and the target signal is determined based on S ₁ and S ₂ .

Optionally, the computing device may comprehensively consider the EEG signals measured by the primary electrode and the secondary electrode when determining the target signal. In other words, the computing device may comprehensively consider the first differential signal and the second differential signal when determining the target signal.

Optionally, considering that the secondary electrode is located on the sound signal emission side and is easily affected by noise, the computing device may ignore the EEG signal measured by the secondary electrode when determining the target signal, that is, ignore the second differential signal.

In other embodiments, if there are multiple other additional electrodes, assuming that in addition to the secondary electrode V _i and the primary electrode V _m , there are N additional reference electrodes V _ref1 , V _ref2 , …, V _refn on the scalp, the computing device may use an automatic re-reference algorithm to determine the target reference signal, and the specific steps are as follows:

Step 1: Calculate the average basic reference voltage V _avg = V _ref1 + V _ref2 + ... + V _refn /n, and calculate V _m - V _avg to obtain the reference signal S _ref .

Step 2: Calculate V _m - V _ref1 to obtain a first differential signal S ₁ , and calculate a standard deviation σ ₁ and a correlation C ₁ between the first differential signal S ₁ and the reference signal S _ref .

It should be noted that if the standard deviation σ ₁ is less than σ _max and C ₁ is greater than the acceptable correlation C _min , the first differential signal is marked as retained; if C ₁ is less than or equal to C _min , or the standard deviation σ ₁ is greater than or equal to σ _max , the first differential signal is marked as discarded. C _min may be 0.8 and σ _max may be 2 μV.

Step 3: perform the same calculation as above on the remaining reference electrodes, for example, calculate V _m -V _refn to obtain the Nth differential signal _Sn , and calculate the standard deviation _σn and correlation _Cn between the Nth differential signal _Sn and the reference signal _Sref .

Similarly, if the standard deviation _σn is less than _σmax and _Cn is greater than the acceptable correlation _Cmin , the Nth differential signal is marked as retained; if _Cn is less than or equal to _Cmin , or the standard deviation _σn is greater than or equal to _σmax , the Nth differential signal is marked as discarded. Wherein, _Cmin can be 0.8 and _σmax can be 2μV.

Step 4: Save the reference signal marked as reserved (ie determine the target signal).

Furthermore, the computing device may also consider the EEG signal measured by the secondary electrode V _i . That is, before the computing device determines the target signal using the automatic re-reference algorithm (i.e., before step 4), the following steps may also be performed:

_Vi - _Vref1 is calculated to obtain a second differential signal _S2 , and a standard deviation _σ2 and a correlation _C2 between the second differential signal _S2 and the reference signal _Sref are calculated.

If the standard deviation σ ₂ is less than σ _max and C ₂ is greater than the acceptable correlation C _min , the second differential signal is marked as retained; if C ₂ is less than or equal to C _min , or the standard deviation σ ₂ is greater than or equal to σ _max , the second differential signal is marked as discarded. C _min may be 0.8 and σ _max may be 2 μV.

It is understandable that if the subject performs other activities during the hearing test, resulting in noise differences or voltage differences in the data collected by each electrode, the correlation between the reference signal S _ref and the first differential signal S ₁ and other signals will become smaller and the variance will become larger, so that such low-quality data can be discarded and data that meets certain requirements can be retained.

In addition, if the subject is tested normally and the signal quality collected by each electrode meets the standard, then N additional electrodes are added for re-reference, and only 1/(N+2) of the test time is required each time to achieve the test effect of the existing technology; or the same test time is used, and the signal-to-noise ratio can reach the existing technology times.

For example, if the traditional method is followed and the test is repeated 900 times, the signal-to-noise ratio increases According to the scheme provided in this application, under ideal conditions (i.e., the subjects are tested normally and the signal quality collected by each electrode meets the standard), using an additional electrode for re-reference only requires 300 tests to achieve the same signal-to-noise ratio as without a reference electrode; if the same 900 tests are performed, the signal-to-noise ratio can be improved by 100% compared with the signal-to-noise ratio without a reference electrode. times.

S307, the computing device processes the target signal to determine the hearing condition of the user.

In some embodiments, the computing device performs filtering and superposition processing on the target signal to determine the characteristic waveform of the user's auditory brainstem evoked potentials (BAEP), and can classify the waveform through a pre-trained classifier to determine the user's current hearing condition.

Exemplarily, FIG5 provides a classification diagram of auditory brainstem evoked potentials (BAEP) for acoustic neuromas. As shown in FIG5 , users' hearing impairment can be divided into four types, including type 1, type 2, type 3 and normal type. Normal users can hear waves I to V, wherein wave I originates from the peripheral part of the cochlear nerve and reflects the action potential of the extracranial segment of the auditory nerve; wave II originates from the cochlear nucleus and is related to the electrical activity of the intracranial segment of the auditory nerve; wave III originates from the superior olivary nucleus and is closely related to the electrical activity of the superior olivary nucleus; wave IV originates from the ventral nucleus of the lateral lemniscus; and wave V originates from the inferior colliculus. Type 1 users cannot hear waves I to V, and this type of user has severe damage to the auditory nerve; type 2 users cannot hear waves II to V, and this type of user has severe damage to the intracranial segment of the auditory nerve or the brainstem; type 3 users have a prolonged interval between waves III and V, suggesting that the lesion may affect the auditory conduction pathway in the brainstem.

It is understandable that when the computing device classifies the waveform, it can be directly compared with the classification diagram shown in, for example, FIG. 5 to determine the user's hearing condition.

Among them, the classifier can be, for example, a neural network, a Bayesian classifier, a support vector machine (SVM), etc., which is not limited in this application.

The hearing detection method provided in the embodiment of the present application realizes the collection of the electrical signals generated by the brain stem after the user's ear receives specific audio stimulation, performs data processing by signal superposition, re-reference and other methods, and then realizes automatic recognition of the electrical signal waveform by machine learning and other methods, thereby automatically realizing an objective assessment of the user's hearing condition. In addition, by adding additional electrodes for automatic re-reference, It can reduce the test time or improve the test accuracy under the same test time.

Fig. 6 is a schematic flow chart of another hearing detection method provided in an embodiment of the present application. The hearing detection method 600 mainly involves the interaction between an audio output device, a signal acquisition device and a computing device, and the method 600 may include S601 to S606.

S601, the computing device detects the connection state and wearing posture of the audio output device and the connection state and wearing posture of the signal collection device.

The specific content of this step can be referred to S301 and will not be described in detail here.

S602, the computing device, the audio output device and the signal acquisition device perform time synchronization.

After detecting that the connection status of the audio output device and the signal acquisition device and the wearing posture meet the conditions, the computing module, audio output module, and signal acquisition module are networked through the soft bus and time synchronization is performed.

It is understandable that when wirelessly measuring the hearing of a user, it is also necessary to synchronize the time of each device with high accuracy (time error <1 ms).

Exemplarily, the master device can initiate time synchronization to each slave device, synchronize the time of the master device to the slave device, and the slave device sends the received time to the master device. After the master device receives the time, it can calculate the delay between the master and the slave device. If the delay error between the master device and each slave device is greater than a preset threshold, the master device time + the calculated delay is sent to each slave device, and the slave device sets its own time to the time calculated by the master device, thereby completing the time synchronization. If the error between the times returned by each slave device to the master device is less than or equal to the preset threshold, it means that the time of each slave device is synchronized. Among them, the master device can be a computing device, and the slave device can include an audio output device and a signal acquisition device.

S603: The computing device sends first information to the audio output device.

S604: The computing device sends second information to the signal collection device.

S603 and S604 may refer to S302 and S303 respectively, and will not be described in detail here.

It should be noted that after the audio output device and the signal acquisition device complete time synchronization, the signal acquisition device starts to collect EEG signals and saves the collected data in groups according to each audio output. Furthermore, when performing wireless measurements, if the time error is found to be greater than the maximum threshold when the computing device synchronizes the timestamp, the EEG data collected last time needs to be discarded and time synchronization needs to be performed again.

Exemplarily, the computing device initiates a wireless measurement of the user's hearing. The audio output device emits sound for 1 second at a time, and the signal acquisition device acquires sound for 3 seconds. For example, the audio output device emits sound when the timestamp of the device is 100000000, and stops emitting sound at 100001000, and sends the timestamp to the computing device; the signal acquisition device starts collecting EEG signals at 100001000, and ends collecting EEG signals at 100004000, and sends the result to the computing device. Based on the received data, the computing device determines that there is a 1 second error in the start time of the two devices, which is greater than the maximum threshold, and then discards this set of data and re-initiates time synchronization.

S605: The audio output device sends the collected first data set to the computing device.

S606: The signal collection device sends the collected second data set to the computing device.

S607: The computing device determines a target signal according to the first data set and the second data set.

S608: The computing device processes the target signal to determine the hearing condition of the user.

The specific contents of S605 to S608 can refer to S304 to S307 respectively, and will not be repeated here.

The hearing detection method provided in the embodiment of the present application can realize wireless measurement of brain wave signals while reducing the test time and improving the accuracy, thereby realizing wireless detection of user hearing.

FIG7 is a schematic block diagram of a hearing detection device provided in an embodiment of the present application. The device 700 shown in FIG7 includes a receiving unit 701 , a sending unit 702 and a processing unit 703 .

The sending unit 702 is used to: send first information to the audio output device, where the first information is used to instruct the audio output device to output a sound signal to the user; and send second information to the signal acquisition device, where the second information is used to instruct the signal acquisition device to acquire an electroencephalogram signal.

The receiving unit 701 is used to receive an electroencephalogram signal sent by a signal acquisition device, where the electroencephalogram signal is an electroencephalogram signal generated by a user in response to a sound signal.

The processing unit 703 is used to determine the user's hearing condition according to the EEG signal.

Optionally, the processing unit 703 is also used to determine the user's hearing condition based on the first signal, the third signal and the fourth signal; wherein the first signal is an electroencephalogram signal obtained by the signal acquisition device measuring the opposite side of the sound signal, the third signal and the fourth signal are electroencephalogram signals obtained by the signal acquisition device measuring the user's scalp, and the measurement position of the third signal is different from the measurement position of the fourth signal.

Optionally, the processing unit 703 is further used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; calculate the average value of the third signal and the fourth signal to obtain a first basic signal; calculate the difference between the first signal and the first basic signal to obtain a first reference signal; and calculate the difference between the first signal and the first basic signal according to the first differential signal, the third differential signal and the fourth signal. The first reference signal determines the hearing condition of the user.

Optionally, the standard deviation of the first differential signal and the first reference signal is less than a first threshold, and the correlation coefficient between the first differential signal and the first reference signal is greater than a second threshold; the standard deviation of the third differential signal and the first reference signal is less than the first threshold, and the correlation coefficient between the third differential signal and the first reference signal is greater than the second threshold.

Optionally, the processing unit 703 is further used to: calculate the difference between the first signal and the third signal to obtain a first differential signal; calculate the difference between the first signal and the fourth signal to obtain a third differential signal; and determine the user's hearing condition based on the first differential signal and the third differential signal.

Optionally, the audio output device is connected to the computing device wirelessly, and the signal acquisition device is connected to the computing device wirelessly.

Optionally, the processing unit 703 is further used to: control the audio output device to output a sound signal from a first moment; control the signal acquisition device to acquire an electroencephalogram signal from a second moment; and the time difference between the first moment and the second moment is less than a fourth threshold.

Optionally, the processing unit 703 is also used to: detect the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device; if it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, output a prompt message to remind the user; if it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device does not meet the wearing standard, output a prompt message to remind the user.

Optionally, the processing unit 703 is further used to: filter and superimpose the EEG signal to determine the waveform of the EEG signal; and classify the waveform of the EEG signal using a preset algorithm to determine the hearing condition of the user.

In addition, an embodiment of the present application also provides a hearing detection system, which may include an audio output device, a signal acquisition device and a computing device.

The audio output device may execute the steps executed by the audio output device in Figures 2, 3 and 6. Exemplarily, the audio output device may be used to receive the first information sent by the computing device; the audio output device may also be used to output a sound signal to one ear of the user.

The signal acquisition device can execute the steps performed by the signal acquisition device in Figures 2, 3 and 6. Exemplarily, the signal acquisition device can be used to receive the second information sent by the computing device; the signal acquisition device can also be used to collect the EEG signal generated by the user; the signal acquisition device can also be used to: transmit the EEG signal to the computing device.

The computing device may execute the steps performed by the computing device in Figures 2, 3, and 6. Exemplarily, the computing device may be used to send first information to an audio output device, the first information being used to instruct the audio output device to output a sound signal to a user; the computing device may also be used to send second information to a signal acquisition device, the second information being used to instruct the signal acquisition device to acquire an electroencephalogram signal; the computing device may also be used to determine the hearing condition of the user based on the electroencephalogram signal.

Fig. 8 is a schematic block diagram of a hearing detection device provided in an embodiment of the present application. The device 800 shown in Fig. 8 includes a memory 801, a processor 802, a communication interface 803 and a bus 804. The memory 801, the processor 802 and the communication interface 803 are connected to each other through the bus 804.

The memory 801 may be a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 801 may store a program. When the program stored in the memory 801 is executed by the processor 802, the processor 802 is used to execute the various steps of the hearing detection method of the embodiment of the present application. For example, the various steps of the embodiments shown in FIG. 2, FIG. 3 and FIG. 6 may be executed.

Processor 802 can adopt a general-purpose CPU, a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs to implement the hearing detection method of the method embodiment of the present application.

The processor 802 may also be an integrated circuit chip with signal processing capability. In the implementation process, each step of the hearing detection method of the embodiment of the present application may be completed by an integrated logic circuit of hardware in the processor 802 or by instructions in the form of software.

The processor 802 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 802 reads the information in the memory 801 and executes the hearing detection method of the embodiment of the method of the present application in combination with its hardware, for example, The various steps/functions of the embodiments shown in FIG. 2 , FIG. 3 and FIG. 6 may be performed.

The communication interface 803 may use, but is not limited to, a transceiver or other transceiver device to implement communication between the apparatus 800 and other devices or a communication network.

The bus 804 may include a path for transmitting information between various components of the device 800 (eg, the memory 801 , the processor 802 , and the communication interface 803 ).

It should also be understood that FIG8 is merely an example and not a limitation, and the communication device including the processor, the memory, and the transceiver may not rely on the structure shown in FIG8 .

In addition, the present application provides a chip, the chip comprising a processor. A memory for storing a computer program is provided independently of the chip, and the processor is used to execute the computer program stored in the memory, so that the operation and/or processing performed by the computing device in any one of the method embodiments is executed.

Furthermore, the chip may further include a data interface. The data interface may be an input/output interface, or an interface circuit, etc. Furthermore, the chip may further include a memory.

The chip in the embodiments of the present application can be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a CPU, a digital signal processing circuit (DSP), a microcontroller (MCU), a programmable logic device (PLD), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or other integrated chips.

The present application also provides a computer program product, which includes: a computer program code, when the computer program code is run on a computer, the computer executes the method of any one of the embodiments shown in Figures 2, 3 and 6.

The present application also provides a computer-readable medium storing a program code. When the program code is executed on a computer, the computer executes a method of any one of the embodiments shown in FIG. 2 , FIG. 3 and FIG. 6 .

In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, and other mature storage media in the art. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync link DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

A hearing detection system, characterized in that the system includes an audio output device, a signal acquisition device and a computing device, wherein:

The audio output device is used to: receive first information sent by the computing device, where the first information is used to instruct the audio output device to output a sound signal to a user;

The signal acquisition device is used to: receive second information sent by the computing device, wherein the second information is used to instruct the signal acquisition device to acquire an electroencephalogram signal, wherein the electroencephalogram signal is an electroencephalogram signal generated by the user in response to the sound signal;

The signal acquisition device is also used to: transmit the EEG signal to the computing device;

The computing device is used to determine the hearing condition of the user according to the EEG signal.
The system according to claim 1, characterized in that

The signal acquisition device comprises a first electrode, a third electrode and a fourth electrode, wherein the first electrode contacts one ear of the user, and the other ear of the user is used to receive the sound signal, the third electrode and the fourth electrode contact the scalp of the user, and the fourth electrode and the third electrode are located at different positions on the scalp of the user,

The EEG signal measured by the first electrode is a first signal, the EEG signal measured by the third electrode is a third signal, and the EEG signal measured by the fourth electrode is a fourth signal;

The computing device is also used to determine the hearing condition of the user according to the first signal, the third signal and the fourth signal.
The system according to claim 2, wherein the computing device is further configured to:

Calculating a difference between the first signal and the third signal to obtain a first differential signal;

Calculating a difference between the first signal and the fourth signal to obtain a third differential signal;

Calculating an average value of the third signal and the fourth signal to obtain a first basic signal;

Calculating a difference between the first signal and the first basic signal to obtain a first reference signal;

The hearing condition of the user is determined according to the first differential signal, the third differential signal and the first reference signal.
The system according to claim 3, characterized in that

A standard deviation between the first differential signal and the first reference signal is smaller than a first threshold, and a correlation coefficient between the first differential signal and the first reference signal is larger than a second threshold;

A standard deviation between the third differential signal and the first reference signal is smaller than the first threshold, and a correlation coefficient between the third differential signal and the first reference signal is larger than the second threshold.
The system according to claim 2, wherein the computing device is further configured to:

Calculating a difference between the first signal and the third signal to obtain a first differential signal;

Calculating a difference between the first signal and the fourth signal to obtain a third differential signal;

The hearing condition of the user is determined according to the first differential signal and the third differential signal.
The system according to any one of claims 1 to 5, characterized in that the signal acquisition device is also used for:

When the signal collection times of the signal collection device is greater than a third threshold, the EEG signal is transmitted to the computing device.
The system according to any one of claims 1-6 is characterized in that the audio output device is connected to the computing device in a wireless manner, and the signal acquisition device is connected to the computing device in a wireless manner.
The system according to claim 7, characterized in that

The audio output device is further used to: output the sound signal from the first moment;

The signal acquisition device is also used to: collect the EEG signal from the second moment;

The time difference between the first moment and the second moment is less than a fourth threshold.
The system according to any one of claims 1 to 8, wherein the computing device is further used for:

Detecting the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device;

If it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, outputting a prompt message to remind the user;

If it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device do not meet the wearing standard, a prompt message is output to remind the user.
The system according to any one of claims 1 to 9, wherein the computing device is further used for:

Performing filtering and superposition processing on the EEG signal to determine the waveform of the EEG signal;

The waveform of the electroencephalogram signal is classified using a preset algorithm to determine the hearing condition of the user.
A hearing detection method, characterized in that the method is applied to a computing device, comprising:

Sending first information to an audio output device, where the first information is used to instruct the audio output device to output a sound signal to a user;

Sending second information to the signal acquisition device, wherein the second information is used to instruct the signal acquisition device to collect EEG signals;

receiving an electroencephalogram signal sent by the signal acquisition device, wherein the electroencephalogram signal is an electroencephalogram signal generated by the user in response to the sound signal;

The hearing condition of the user is determined according to the EEG signal.
The method according to claim 11, characterized in that determining the hearing condition of the user according to the EEG signal comprises:

determining the hearing condition of the user according to the first signal, the third signal and the fourth signal;

Among them, the first signal is the EEG signal obtained by the signal acquisition device measuring the opposite side of the sound signal, the third signal and the fourth signal are the EEG signals obtained by the signal acquisition device measuring the scalp of the user, and the measurement position of the third signal is different from the measurement position of the fourth signal.
The method according to claim 12, characterized in that determining the hearing condition of the user according to the first signal, the third signal and the fourth signal comprises:

Calculating a difference between the first signal and the third signal to obtain a first differential signal;

Calculating a difference between the first signal and the fourth signal to obtain a third differential signal;

Calculating an average value of the third signal and the fourth signal to obtain a first basic signal;

Calculating a difference between the first signal and the first basic signal to obtain a first reference signal;

The hearing condition of the user is determined according to the first differential signal, the third differential signal and a first reference signal.
The method according to claim 13, characterized in that

A standard deviation between the first differential signal and the first reference signal is smaller than a first threshold, and a correlation coefficient between the first differential signal and the first reference signal is larger than a second threshold;

A standard deviation between the third differential signal and the first reference signal is smaller than the first threshold, and a correlation coefficient between the third differential signal and the first reference signal is larger than the second threshold.
The method according to claim 12, characterized in that determining the hearing condition of the user according to the first signal, the third signal and the fourth signal comprises:

Calculating a difference between the first signal and the third signal to obtain a first differential signal;

Calculating a difference between the first signal and the fourth signal to obtain a third differential signal;

The hearing condition of the user is determined according to the first differential signal and the third differential signal.
The method according to any one of claims 11-15 is characterized in that the audio output device is connected to the computing device in a wireless manner, and the signal acquisition device is connected to the computing device in a wireless manner.
The method according to claim 16, characterized in that the method further comprises:

Controlling the audio output device to output the sound signal from a first moment;

Controlling the signal acquisition device to start acquiring the EEG signal from the second moment;

The time difference between the first moment and the second moment is less than a fourth threshold.
The method according to any one of claims 11 to 17, characterized in that before sending the first information to the audio output device, the method further comprises:

Detecting the connection status and wearing posture of the audio output device and the connection status and wearing posture of the signal acquisition device;

If it is detected that the audio output device is not connected to the computing device and/or the signal acquisition device is not connected to the computing device, outputting a prompt message to remind the user;

If it is detected that the wearing posture of the audio output device and/or the wearing posture of the signal acquisition device do not meet the wearing standard, a prompt message is output to remind the user.
The method according to any one of claims 11 to 18, characterized in that determining the hearing condition of the user according to the EEG signal comprises:

Performing filtering and superposition processing on the EEG signal to determine the waveform of the EEG signal;

The waveform of the electroencephalogram signal is classified using a preset algorithm to determine the hearing condition of the user.
A hearing detection device, characterized in that it comprises: a processor, the processor is coupled to a memory, the memory is used to store programs or instructions, when the program or instructions are executed by the processor, the hearing detection device executes the method as described in any one of claims 11 to 19.
A chip, characterized in that it comprises: a processor and a data interface, wherein the processor reads instructions stored in a memory through the data interface to execute the method as described in any one of claims 11 to 19.
A computer-readable storage medium, characterized in that instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer executes the method as described in any one of claims 11 to 19.